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        <title>Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles</title>
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            <namePart type="given">Juan</namePart>
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    <abstract>We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-versatile, we compare ideology labels from expert human annotators, GPT-4o-mini (baseline and finetuned), and Llama-3.3-70B. We apply Double Machine Learning (DML) and community-level mediation analysis across all four annotation paradigms. Human annotations yield no significant causal effects at the community level. Fine-tuned GPT-4o-mini achieves the highest classification accuracy (F1=72.48) and is the only annotator paradigm that produces significant community-level treatment effects and significant natural direct effects (NDEs) in mediation. We interpret this as evidence of shortcut learning: fine-tuning on ideology-labeled data causes the model to internalise a spurious sentiment–ideology coupling not operative in human judgment for this task. This coupling is structurally invisible to F1-based evaluation, with implications for the use of LLM annotations as silver labels and as proxies for human judgment in downstream causal analyses.</abstract>
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%0 Conference Proceedings
%T Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles
%A Chatterjee, Upasana
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F chatterjee-2026-topic
%X We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label. Using articles from AllSides, paired with shared sentiment annotations from Llama-3.3-70b-versatile, we compare ideology labels from expert human annotators, GPT-4o-mini (baseline and finetuned), and Llama-3.3-70B. We apply Double Machine Learning (DML) and community-level mediation analysis across all four annotation paradigms. Human annotations yield no significant causal effects at the community level. Fine-tuned GPT-4o-mini achieves the highest classification accuracy (F1=72.48) and is the only annotator paradigm that produces significant community-level treatment effects and significant natural direct effects (NDEs) in mediation. We interpret this as evidence of shortcut learning: fine-tuning on ideology-labeled data causes the model to internalise a spurious sentiment–ideology coupling not operative in human judgment for this task. This coupling is structurally invisible to F1-based evaluation, with implications for the use of LLM annotations as silver labels and as proxies for human judgment in downstream causal analyses.
%U https://aclanthology.org/2026.acl-srw.65/
%P 725-740

Markdown (Informal)

[Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles](https://aclanthology.org/2026.acl-srw.65/) (Chatterjee, ACL 2026)

• Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles (Chatterjee, ACL 2026)

ACL

• Upasana Chatterjee. 2026. Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 725–740, San Diego, California, United States. Association for Computational Linguistics.